.png){kind=icon}
返回
Deep learning models for digital image processing: a review
.png){kind=icon}
OA
AI
DOI：10.1007/s10462-023-10631-z.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Within the domain of image processing, a wide array of methodologies is dedicated to tasks including denoising, enhancement, segmentation, feature extraction, and classification. These techniques collectively address the challenges and opportunities posed by different aspects of image analysis and manipulation, enabling applications across various fields. Each of these methodologies contributes to refining our understanding of images, extracting essential information, and making informed decisions based on visual data. Traditional image processing methods and Deep Learning (DL) models represent two distinct approaches to tackling image analysis tasks. Traditional methods often rely on handcrafted algorithms and heuristics, involving a series of predefined steps to process images. DL models learn feature representations directly from data, allowing them to automatically extract intricate features that traditional methods might miss. In denoising, techniques like Self2Self NN, Denoising CNNs, DFT-Net, and MPR-CNN stand out, offering reduced noise while grappling with challenges of data augmentation and parameter tuning. Image enhancement, facilitated by approaches such as R2R and LE-net, showcases potential for refining visual quality, though complexities in real-world scenes and authenticity persist. Segmentation techniques, including PSPNet and Mask-RCNN, exhibit precision in object isolation, while handling complexities like overlapping objects and robustness concerns. For feature extraction, methods like CNN and HLF-DIP showcase the role of automated recognition in uncovering image attributes, with trade-offs in interpretability and complexity. Classification techniques span from Residual Networks to CNN-LSTM, spotlighting their potential in precise categorization despite challenges in computational demands and interpretability. This review offers a comprehensive understanding of the strengths and limitations across methodologies, paving the way for informed decisions in practical applications. As the field evolves, addressing challenges like computational resources and robustness remains pivotal in maximizing the potential of image processing techniques.
Keyword:
Image processing
Deep learning models
Convolutional neural networks (CNN)
期刊
IF:
13.9
论文数:
6.1K
被引数:
1.9W
机构
引用论文
A deep learning method with wrapper based feature extraction for wireless intrusion detection system一种基于包装器的无线入侵检测特征提取深度学习方法.png)
COMPUTERS & SECURITY
COMPUTERS & SECURITYIF5.4
PREAI
分享收藏Image Enhancement for Tuberculosis Detection Using Deep Learning基于深度学习的结核病检测图像增强IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Semantic segmentation of the micro-structure of strain-hardening cement-based composites (SHCC) by applying deep learning on micro-computed tomography scans通过在微计算机断层扫描中应用深度学习对应变硬化水泥基复合材料 (SHCC) 的微观结构进行语义分割
PREAI
分享收藏Melanoma segmentation using deep learning with test-time augmentations and conditional random fieldsSCIENTIFIC REPORTSOAAI
SCIENTIFIC REPORTSIF3.9
OAAI分享收藏
查看更多内容